Internet Appendix: High Frequency Trading and Extreme Price Movements

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1 Internet Appendix: High Frequency Trading and Extreme Price Movements This appendix includes two parts. First, it reports the results from the sample of EPMs defined as the 99.9 th percentile of raw returns. Second, it details the estimation of the Lee and Mykland (2012) jump identification algorithm and reports the results for EPMs defined using this algorithm. 1. EPM sample based on the 99.9 th percentile of raw returns As an alternative to the procedure that defines EPMs as the 99.9 th percentile of residuals from Eq. (1), we define EPMs as the 99.9 th percentile of raw returns. The results from this sample are reported in Tables A1-A7. The results obtained from both samples are qualitatively similar to those discussed in the main manuscript. 2. EPM sample based on the Lee-Mykland jump identification algorithm The Lee and Mykland (2012) algorithm (LM) identifies intervals with discrete price changes using the non-parametric approach based on realized volatility. First, the optimal sampling frequency is identified as for the 1 -dependent noise. In our sample, the second-by-second midpoint return observations tend to follow an MA process with many statistically significant dependent AR lags. Nonetheless, the magnitude of the lag coefficients tends to drop sharply after 6 8 lags. To make the estimated results comparable across stocks, we select 10. The algorithm suggests that a researcher should pre-average over M sampled observations before computing the jump statistics as follows: /, where is a parameter defined by the noise variance, and is the number of observations in the jump 1

2 estimation period. Recognizing that in a high-frequency sample the realized variation of logprices converges to noise variance, we estimate the volatility of noise as: /2 (A1) Following LM, we split each trading day into seven intervals: 9:30 10:00, 10:00 11:00, 11:00 12:00, 12:00 13:00, 13:00 14:00, 14:00 15:00, and 15:00 16:00. The estimated noise variance for these intervals is reported in Table A8. Based on the estimates, we choose 1/19, and therefore the estimated is close to one for all estimation periods. 1 Next, the standardized statistic for jump detection is defined as follows:, (A2) where, and is the estimate of the total variance. The total variance is a sum of the estimated noise variance and price volatility. We use the noise- and outlierrobust bipower variation of Christensen, Oomen and Podolskij (2014) as the measure of price volatility. From LM s Theorem 1, the null hypothesis of no jump in a given interval is rejected if:, (A3) where 2 (A4) 1 Since we use midpoint prices, much of the noise coming from the bid-ask bounce is mitigated, and the remaining noise does not produce the estimate high enough to make pre-sampling useful. 2

3 and. (A5) The rejection threshold is selected from the standard Gumbel distribution, which implies that β log log 1 α. We use the significance level of α 5%, leading to β The above procedure results in rejecting the no-jump hypothesis for 0.54% of sample observations. We restrict the LM sample to include the same number of EPMs as the 99.9 sample based on raw returns, or 45,406 instances, using the highest-magnitude LM jumps for each stock. In Tables A9 through A15, we use the LM sample to replicate the results reported in the main manuscript. Overall, the results in the LM sample are similar to those discussed in the main manuscript. References: Christensen, K., Oomen, R. C. A., Podolskij, M., Fact or friction: Jumps at ultra highfrequency. Journal of Financial Economics 114, Lee, S. S., Mykland, P. A., Jumps in equilibrium prices and market microstructure noise. Journal of Econometrics 168,

4 Table A1 Summary statistics The table reports summary statistics for the sample of extreme price movements (EPMs). is the absolute value of the 10-second midpoint return. is the number of (HFT) trades during the interval. and are the total dollar and share volume traded during the interval. and are quoted and relative quoted NBBO spreads, respectively, in dollars and percentage points. All statistics are averaged over the 10-second sampling intervals. Mean Median Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 473, ,158 1,024,504 Share volume 15,595 5,431 31,734 Quoted spread, $ Relative spread, % N 45,406 4

5 Table A2 Liquidity supply and demand around EPMs The table reports directional trading volume around extreme price movements. Time interval t is the 10-second EPM interval. In addition, we report the results for the two time intervals preceding the EPM and two subsequent time intervals. HFT D (nhft D ) is the difference in liquidity-demanding HFT (nhft) volume in the direction of the EPM and liquidity-demanding volume against the direction of the EPM. HFT S (nhft S ) is the difference in liquidity-providing volume against the direction of the EPM and liquidity-providing volume in the direction of the EPM. HFT NET (nhft NET ) is the difference between HFT D and HFT S (nhft D and nhft S ). -Values are in parentheses. *** and ** indicate statistical significance at the 1% and 5% levels. t-20 t-10 t t+10 t+20 HFT NET ** *** *** -42.7** (0.94) (0.04) (0.04) HFT D *** *** *** -99.1*** (0.13) HFT S *** *** 156.5*** 56.4*** (0.14) nhft NET ** 299.3*** 122.5*** 42.7** (0.94) (0.04) (0.04) nhft D 75.3** 326.7*** *** 672.4*** 317.0*** (0.03) nhft S -76.8** *** *** *** *** (0.02) 5

6 Table A3 Transitory and permanent EPMs The table reports summary statistics for transitory and permanent EPMs. Transitory EPMs revert by more than 2/3 of the EPM return in the following 30 minutes. Permanent EPMs do not revert by more than 1/3 in the same interval. Because we exclude EPMs that revert by the amount between 1/3 and 2/3, the total number of EPMs in this table is 87.60% of that reported in Panel A of Table A2. Panel B reports HFT NET around the two EPM types. Asterisks *** indicate statistical significance at the 1% level. Transitory Permanent Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 465,100 1,058, , ,642 Share volume 14,716 29,412 14,652 29,098 Quoted spread, $ Relative spr., % N 18,185 21,116 t-20 t-10 t t+10 t+20 Transitory *** *** *** Permanent *** *** *** 2.0 6

7 Table A4 EPM magnitude quartiles Panel A divides EPMs into quartiles by return magnitude, from smallest to largest. Panel B contains HFT NET statistics. Asterisks ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels. Q1 (small) Q2 Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 378, , , ,376 Share volume 12,487 24,759 12,973 24,216 Quoted spread, $ Relative spr., % N 11,358 11,327 Q3 Q4 (large) Absolute return, % Total trades Total HFT trades Dollar volume 452, , ,912 1,356,125 Share volume 15,070 30,330 21,842 43,031 Quoted spread, $ Relative spr., % N 11,358 11,363 t-20 t-10 t t+10 t+20 Q * *** 4.9 Q *** *** -82.2** -61.8* Q *** -82.8* Q *** ***

8 Table A5 Standalone and co-epms Panel A divides EPMs into standalone and co-epms, with the latter group capturing EPMs that occur simultaneously in several stocks. Panel B contains HFT NET statistics. Asterisks *** and ** indicate statistical significance at the 1% and 5% levels. Standalone Co-EPMs Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 625,553 1,272, , ,887 Share volume 21,368 40,535 11,280 22,092 Quoted spread, $ Relative spr., % # Stocks N 19,424 25,982 t-20 t-10 t t+10 t+20 Standalone *** *** Co-EPMs *** 446.4*** *** -44.1** 8

9 Table A6 Net HFT activity and EPMs The table reports estimated coefficients from the following regression: 1, where HFT NET is the difference between HFT D and HFT S ; the dummy 1 EPM is equal to one if a 10- second interval t is identified to contain an EPM and is equal to zero otherwise; 1 EPM-TRANSITORY and 1 EPM-PERMANENT are dummies that capture the two EPM types; 1 EPM-STANDALONE captures the standalone EPMs; 1 CO-EPM captures EPMs that occur simultaneously in two or more sample stocks; 1 EPM-Q1 through 1 EPM-Q4 identify four EPM quartiles by magnitude, from the smallest to the largest; Ret is the absolute return; Vol is the total trading volume; Spr is the percentage quoted spread; and is a vector of lags of the dependent variable and each of the independent variables, with 1,2,,10 and the variables indexed with a subscript. All non-dummy variables are standardized on the stock level. Regressions are estimated with stock fixed effects. -Values associated with the double-clustered standard errors are in parentheses. *** denote statistical significance at the 1% level. (1) (2) (3) (4) 1 EPM *** 1 EPM-TRANSITORY *** 1 EPM-PERMANENT *** 1 EPM-STANDALONE *** 1 CO-EPM *** 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** Ret 0.072*** 0.072*** 0.072*** 0.073*** Vol 0.081*** 0.081*** 0.081*** 0.081*** Spr *** *** *** *** Adj. R

10 Table A7 EPM determinants The table reports the coefficients and the marginal effects from a probit model of EPM occurrence: 1, where the dependent variable is equal to one if an interval contains an extreme price movement and zero otherwise. All independent variables are lagged by one interval. HFT NET is the share volume traded in the direction of the price movement minus the share volume traded against the direction of the price movement for all HFT trades, Ret is the absolute return, Vol is total traded volume, Spr is the percentage quoted spread. All variables are standardized on the stock level. The marginal effects are scaled by a factor of 1,000. -Values are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels. All Standalone Co-EPMs Permanent Transitory (1) (2) (3) (4) (5) Intercept *** *** *** *** *** HFT NET t *** *** * *** Marginal Effect (0.42) (0.09) Controls Yes Yes Yes Yes Yes Pseudo-R

11 Table A8 Noise volatility The table reports estimated noise volatility q as a percentage of price used for computation of the LM statistic. The noise variance is estimated by intraday period, day and stock. Period q Max Std. Dev. 9:30 10: % 0.095% 0.014% 10:00 11: % 0.068% 0.010% 11:00 12: % 0.051% 0.008% 12:00 13: % 0.043% 0.006% 13:00 14: % 0.042% 0.006% 14:00 15: % 0.047% 0.007% 15:00 16: % 0.054% 0.008% 11

12 Table A9 Summary statistics The table reports summary statistics for the sample of extreme price movements (EPMs). is the absolute value of the 10-second midpoint return. is the number of (HFT) trades during the interval. and are the total dollar and share volume traded during the interval. and are quoted and relative quoted NBBO spreads, respectively, in dollars and percentage points. All statistics are averaged over the 10-second sampling intervals. Mean Median Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 531, ,249 1,007,435 Share volume 16,688 6,189 31,989 Quoted spread, $ Relative spread, % N 45,400 12

13 Table A10 Liquidity supply and demand around EPMs The table reports directional trading volume around extreme price movements. Time interval t is the 10-second EPM interval. In addition, we report the results for the two time intervals preceding the EPM and two subsequent time intervals. HFT D (nhft D ) is the difference in liquidity-demanding HFT (nhft) volume in the direction of the EPM and liquidity-demanding volume against the direction of the EPM. HFT S (nhft S ) is the difference in liquidity-providing volume against the direction of the EPM and liquidity-providing volume in the direction of the EPM. HFT NET (nhft NET ) is the difference between HFT D and HFT S (nhft D and nhft S ). -Values are in parentheses. *** and ** indicate statistical significance at the 1% and 5% levels. t-20 t-10 t t+10 t+20 HFT NET 30.5** 40.6** *** -65.1*** 7.1 (0.04) (0.02) (0.66) HFT D *** *** *** *** (0.53) HFT S *** *** 297.9*** 153.2*** (0.14) nhft NET -30.5** -40.6** 892.6*** 65.1*** -7.1 (0.04) (0.02) (0.66) nhft D *** *** 478.9*** 145.8*** (0.84) nhft S *** *** *** *** (0.14) 13

14 Table A11 Transitory and permanent EPMs The table reports summary statistics for transitory and permanent EPMs. Transitory EPMs revert by more than 2/3 of the EPM return in the following 30 minutes. Permanent EPMs do not revert by more than 1/3 in the same interval. Because we exclude EPMs that revert by the amount between 1/3 and 2/3, the total number of EPMs in this table is 87.60% of that reported in Panel A of Table A2. Panel B reports HFT NET around the two EPM types. Asterisks *** and ** indicate statistical significance at the 1% and 5% levels. Transitory Termanent Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 513,793 1,014, , ,796 Share volume 15,400 28,963 14,929 27,001 Quoted spread, $ Relative spr., % N 18,249 21,523 Panel B: HFT NET t-20 t-10 t t+10 t+20 Transitory *** -75.1*** -3.4 Permanent *** -61.6**

15 Table A12 EPM magnitude quartiles Panel A divides EPMs into quartiles by return magnitude, from smallest to largest. Panel B contains HFT NET statistics. Asterisks ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels. Q1 (small) Q2 Mean Std. dev. Mean Std. Dev. Absolute return, % Total trades Total HFT trades Dollar volume 429, , , ,772 Share volume 12,861 22,658 14,437 27,024 Quoted spread, $ Relative spr., % N 11,313 11,366 Q3 Q4 (large) Absolute return, % Total trades Total HFT trades Dollar volume 512, , ,441 1,403,664 Share volume 16,194 28,929 23,240 44,130 Quoted spread, $ Relative spr., % N 11,355 11,366 t-20 t-10 t t+10 t+20 Q *** Q2 81.7*** *** Q * *** -77.3** 25.7 Q *** ***

16 Table A13 Standalone and co-epms Panel A divides EPMs into standalone and co-epms, with the latter group capturing EPMs that occur simultaneously in several stocks. Panel B contains HFT NET statistics. Asterisks *** and ** indicate statistical significance at the 1% and 5% levels. Standalone Co-EPMs Mean Std. dev. Mean Std. dev. Absolute return, % Total trades Total HFT trades Dollar volume 590,320 1,091, , ,154 Share volume 18,342 33,310 13,552 29,064 Quoted spread, $ Relative spr., % # Stocks N 29,724 15,676 t-20 t-10 t t+10 t+20 Standalone *** -62.2** 27.9 Co-EPMs *** 850.7*** -70.6***

17 Table A14 Net HFT activity and EPMs The table reports estimated coefficients from the following regression: 1, where HFT NET is the difference between HFT D and HFT S ; the dummy 1 EPM is equal to one if a 10- second interval t is identified to contain an EPM and is equal to zero otherwise; 1 EPM-TRANSITORY and 1 EPM-PERMANENT are dummies that capture the two EPM types; 1 EPM-STANDALONE captures the standalone EPMs; 1 CO-EPM captures EPMs that occur simultaneously in two or more sample stocks; 1 EPM-Q1 through 1 EPM-Q4 identify four EPM quartiles by magnitude, from the smallest to the largest; Ret is the absolute return; Vol is the total trading volume; Spr is the percentage quoted spread; and is a vector of lags of the dependent variable and each of the independent variables, with 1,2,,10 and the variables indexed with a subscript. All non-dummy variables are standardized on the stock level. Regressions are estimated with stock fixed effects. -Values associated with the double-clustered standard errors are in parentheses. *** denote statistical significance at the 1% level. (1) (2) (3) (4) 1 EPM *** 1 EPM-TRANSITORY *** 1 EPM-PERMANENT *** 1 EPM-STANDALONE *** 1 CO-EPM (0.07) 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** 1 EPM-Q *** Ret 0.072*** 0.072*** 0.071*** 0.073*** Vol 0.083*** 0.083*** 0.083*** 0.083*** Spr *** *** *** *** Adj. R

18 Table A15 EPM determinants The table reports the coefficients and the marginal effects from a probit model of EPM occurrence: 1, where the dependent variable is equal to one if an interval contains an extreme price movement and zero otherwise. All independent variables are lagged by one interval. HFT NET is the share volume traded in the direction of the price movement minus the share volume traded against the direction of the price movement for all HFT trades, Ret is the absolute return, Vol is total traded volume, Spr is the percentage quoted spread. All variables are standardized on the stock level. The marginal effects are scaled by a factor of 1, Values are in parentheses. *** and * indicate statistical significance at the 1% and 10% levels. All Standalone Co-EPMs Permanent Transitory (1) (2) (3) (4) (5) Intercept *** *** *** *** *** HFT NET t *** * Marginal effect (0.58) (0.21) (0.06) (0.18) Controls Yes Yes Yes Yes Yes Pseudo-R

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